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e-book Subsidence Due to Groundwater Withdrawal in Kathmandu Basin Detec...

저자
P. V. Suresh Krishna...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782697
In recent years, subsidence due to excessive groundwater withdrawal is a major problem in the Kathmandu Basin. In addition, on 25 April 2015, the basin experienced large crustal displacements caused by Mw 7.8 Gorkha earthquake. In this study, we applied StaMPS- Persistent Scatterer InSAR (StaMPS PS-InSAR) technique to estimate the spatio-temporal displacements in the basin after the mainshock. 34 Sentinel-1 C-band SAR data are used for measuring subsidence velocity during 2015-2017. We found the maximum subsidence velocity of about 9.02 cm/year and mean subsidence rate of about 8.06 cm/year in the line of sight direction, respectively, in the central part of the basin.

e-book A Method for Text Information Separation from Floorplan Using SIF...

저자
Yong-hee Shin , Jung...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782682
With the development of data analysis methods and data processing capabilities, semantic analysis of floorplans has been actively studied. Therefore, studies for extracting text information from drawings have been conducted for semantic analysis. However, existing research that separates rasterized text from floorplan has the problem of loss of text information, because when graphic and text components overlap, text information cannot be extracted. To solve this problem, this study defines the morphological characteristics of the text in the floorplan, and classifies the class of the corresponding region by applying the class of the SIFT key points through the SVM models. The algorithm developed in this study separated text components with a recall of 94.3% in five sample drawings.

e-book Selecting Significant Wavelengths to Predict Chlorophyll Content ...

저자
Sung Hyuk Jang , Yon...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,700원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782677
This study was performed to select the significant wavelengths for predicting the chlorophyll content of grafted cucumber seedlings using hyperspectral images. The visible and near-infrared (VNIR) images and the short-wave infrared images of cucumber cotyledon samples were measured by two hyperspectral cameras. A correlation coefficient spectrum (CCS), a stepwise multiple linear regression (SMLR), and partial least squares (PLS) regression were used to determine significant wavelengths. Some wavelengths at 501, 505, 510, 543, 548, 619, 718, 723, and 727 nm were selected by CCS, SMLR, and PLS as significant wavelengths for estimating chlorophyll content. The results from the calibration models built by SMLR and PLS showed fair relationship between measured and predicted chlorophyll concentration. It was concluded that the hyperspectral imaging technique in the VNIR region is suggested effective for estimating the chlorophyll content of grafted cucumber leaves, non-destructively.

e-book Analysis of Land Cover Changes Based on Classification Result Usi...

저자
Byunghyun Yoon , Jae...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782662
Compared to the imagery produced by traditional satellites, PlanetScope satellite imagery has made it possible to easily capture remotely-sensed imagery every day through dozens or even hundreds of satellites on a relatively small budget. This study aimed to detect changed areas and update a land cover map using a PlanetScope image. To generate a classification map, pixel-based Random Forest (RF) classification was performed by using additional features, such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI). The classification result was converted to vector data and compared with the existing land cover map to estimate the changed area. To estimate the accuracy and trends of the changed area, the quantitative quality of the supervised classification result using the PlanetScope image was evaluated first. In addition, the patterns of the changed area that corresponded to the classification result were analyzed using the PlanetScope satellite image. Experimental results found that the PlanetScope image can be used to effectively to detect changed areas on largescale land cover maps, and supervised classification results can update the changed areas.

e-book Optimizing Image Size of Convolutional Neural Networks for Produc...

저자
Hyun-woo Jo , Ji-won...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782657
This study aims to develop a methodology of convolutional neural networks (CNNs) to produce thematic maps from remote sensing data. Optimizing the image size for CNNs was studied, since the size of the image affects to accuracy, working as hyper-parameter. The selected study area is Mt. Ung, located in Dangjin-si, Chungcheongnam-do, South Korea, consisting of both coniferous forest and deciduous forest. Spatial structure analysis and the classification of forest type using CNNs was carried in the study area at a diverse range of scales. As a result of the spatial structure analysis, it was found that the local variance (LV) was high, in the range of 7.65 m to 18.87 m, meaning that the size of objects in the image is likely to be with in this range. As a result of the classification, the image measuring 15.81 m, belonging to the range with highest LV values, had the highest classification accuracy of 85.09%. Also, there was a positive correlation between LV and the accuracy in the range under 15.81 m, which was judged to be the optimal image size. Therefore, the trial and error selection of the optimum image size could be minimized by choosing the result of the spatial structure analysis as the starting point. This study estimated the optimal image size for CNNs using spatial structure analysis and found that this can be used to promote the application of deep-learning in remote sensing.

e-book Vicarious Calibration-based Robust Spectrum Measurement for Spect...

저자
Junhwa Chi
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,600원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782642
The aim of this study is to develop a protocol for obtaining spectral signals that are robust to varying lighting conditions, which are often found in the Polar regions, for creating a spectral library specific to those regions. Because hyperspectral image (HSI)-derived spectra are collected on the same scale as images, they can be directly associated with image data. However, it is challenging to find precise and robust spectra that can be used for a spectral library from images taken under different lighting conditions. Hence, this study proposes a new radiometric calibration protocol that incorporates radiometric targets with a traditional vicarious calibration approach to solve issues in image-based spectrum measurements. HSIs obtained by the proposed method under different illumination levels are visually uniform and do not include any artifacts such as stripes or random noise. The extracted spectra capture spectral characteristics such as reflectance curve shapes and absorption features better than those that have not been calibrated. The results are also validated quantitatively. The calibrated spectra are shown to be very robust to varying lighting conditions and hence are suitable for a spectral library specific to the Polar regions.

e-book Seasonal and Look-directional Variation of X-band SAR Sigma Nough...

저자
Jae-hun Kim , Sun Yo...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782637
This paper presents TerraSAR-X and KOMPSAT-5 sigma nought variation according to season and antenna observation configuration in Mongolia. Two types of landcover including bare surface and cropland were examined. The seasonal variation of sigma nought in cropland was about 7 dB and particularly a significant sigma nought reduction occurred after harvest. On the contrary, the Mongolia bare surface provides a consistent sigma nought values for several years with an annual variation less than 2.5 dB of standard deviation. However, the bare soil was relatively sensitive to look-direction (or ascending or descending mode) as well as incidence angle while the cropland was almost independent of antenna look-direction and small incidence angle changes. Although the look-directional variation of bare surface sigma nought was observed in this study, the look-direction anisotropic nature of the surface was not well examined. A further study would be required to account for this feature with various SAR observation configurations.

e-book Mapping Snow Depth Using Moderate Resolution Imaging Spectroradio...

저자
Daeseong Kim , Hyung...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,900원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782622
In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

e-book Artificial Neural Network-based Model for Predicting Moisture Con...

저자
Tapash Kumar Sarkar ...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,900원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782617
The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values (R2 = 0.743, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).

e-book Accuracy Assessment of Global Land Cover Datasets in South Korea

저자
Sanghun Son , Jinsoo...
출판사
한국학술정보
발행일
2018.08.31
가격
전자책 : 4,000원
ISBN
ISSN
UCI
I410-ECN-0102-2018-400-003782602
The national accuracy of global land cover (GLC) products is of great importance to ecological and environmental research. However, GLC products that are derived from different satellite sensors, with differing spatial resolutions, classification methods, and classification schemes are certain to show some discrepancies. The goal of this study is to assess the accuracy of four commonly used GLC datasets in South Korea, GLC2000, GlobCover2009, MCD12Q1, and GlobeLand30. First, we compared the area of seven classes between four GLC datasets and a reference dataset. Then, we calculated the accuracy of the four GLC datasets based on an aggregated classification scheme containing seven classes, using overall, producer’s and user’s accuracies, and kappa coefficient. GlobeLand30 had the highest overall accuracy (77.59%). The overall accuracies of MCD12Q1, GLC2000, and GlobCover2009 were 75.51%, 68.38%, and 57.99%, respectively. These results indicate that GlobeLand30 is the most suitable dataset to support a variety of national scientific endeavors in South Korea.